.png){kind=icon}
.png){kind=icon}
Return
Multi-instance partial-label learning: towards exploiting dual inexact supervision
.png){kind=icon}
PRE
AI
DOI：10.1007/s11432-023-3771-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label learning. However, in some real-world tasks, each training sample is associated with not only multiple instances but also a candidate label set that contains one ground-truth label and some false positive labels. Specifically, at least one instance pertains to the ground-truth label while no instance belongs to the false positive labels. In this paper, we formalize such problems as multi-instance partial-label learning (MIPL). Existing multi-instance learning algorithms and partial-label learning algorithms are suboptimal for solving MIPL problems since the former fails to disambiguate a candidate label set, and the latter cannot handle a multi-instance bag. To address these issues, a tailored algorithm named MiplGp, i.e., multi-instance partial-label learning with Gaussian processes, is proposed. MiplGp first assigns each instance with a candidate label set in an augmented label space, then transforms the candidate label set into a logarithmic space to yield the disambiguated and continuous labels via an exclusive disambiguation strategy, and last induces a model based on the Gaussian processes. Experimental results on various datasets validate that MiplGp is superior to well-established multi-instance learning and partial-label learning algorithms for solving MIPL problems.
Keywords:
machine learning
multi-instance partial-label learning
multi-instance learning
partial-label learning
Gaussian processes
Journal
IF:
7.6
Papers:
4.9K
Citations:
8.9K